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Abstract: Many control problems take place in continuous state-action spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning o#ers a theoretical framework to learn such control policies from scratch, its applicability to higher dimensional continuous state-action spaces remains rather limited to date. Instead of learning from scratch, in this paper we suggest to learn... (Update)
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BibTeX entry: (Update)
Ijspeert, A., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In S. Becker, S.T., Obermayer, K., eds.: Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, MA (2003) 1523--1530 http://citeseer.ist.psu.edu/article/ijspeert03learning.html More
@misc{ ijspeert03learning,
author = "A. Ijspeert and J. Nakanishi and S. Schaal",
title = "Learning attractor landscapes for learning motor primitives",
text = "Ijspeert, A., Nakanishi, J., Schaal, S.: Learning attractor landscapes
for learning motor primitives. In S. Becker, S.T., Obermayer, K., eds.:
Advances in Neural Information Processing Systems 15. MIT Press, Cambridge,
MA (2003) 1523--1530",
year = "2003",
url = "citeseer.ist.psu.edu/article/ijspeert03learning.html" }
Citations (may not include all citations):614 Reinforcement learning: an introduction - Sutton, Barto - 1998
31 Learning from demonstration - Schaal - 1997
27 Constructive incremental learning from only local informatio.. - Schaal, Atkeson - 2047
15 A kendama learning robot based on bi-directional theory (context) - Miyamoto, Schaal et al. - 1996
11 Using humanoid robots to study human behavior - Atkeson, Hale et al. - 2000
6 Movement imitation with nonlinear dynamical systems in human.. - Ijspeert, Nakanishi et al.
5 Learning rhythmic movements by demonstration using nonlinear.. - Ijspeert, Nakanishi et al.
3 Interpolation for input torque patterns obtained through lea.. (context) - Kawamura, Fukao - 1994
3 Nonlinear force fields: a distributed system of control prim.. (context) - Mussa-Ivaldi - 1997
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